Survey on Secure Mining of Association Rule in Distributed Databases

Abstract -- Discovering frequent item sets is a needful amongest the most fields of data mining. Numerous current data mining tasks can be fulfilled effectively just in a distributed setting. The field of distributed data mining has consequently increased expanding significance in the last decade. Mining frequent itemsets in data streams is an essential task in stream data mining. In this paper, we have taken a review on securing rules in transaction databases. Proficient algorithms for mining frequent itemsets are critical for mining association rules and additionally for numerous other task of data mining. Techniques for mining frequent itemsets have been executed utilizing a Bit Table structure. Apriori Algorithm is the most prevalent and valuable algorithm of Association Rule Mining of Data Mining. As Association rule of data mining is utilized as a part of all utilizations of business and industry. The main objective of taking Apriori is to discover the frequent itemsets and to reveal the hidden data. Various methods are used for solving the problem of securing mining of association rules. In the previous study, it has been shown the issue of finding association rules between items in an expansive database. New algorithms used for tackling this issue that are in a far-reaching way different from the known algorithms. Study result demonstrates that these algorithms beat the known algorithms by components ranging from various little issues to more than a request of magnitude for huge issues.

Keywords -- Association rule; data mining; distributed, data mining; databases, frequent item sets.

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About the Author

Pankaj P. Joshi

1.Pankaj P. Joshi

Department of Computer Engineering,

MIT Academy of Engineering, University Of Pune,

Pune, India

2.Prof. R.M.Goudar

Department of Computer Engineering,

MIT Academy of Engineering, University Of Pune,

Pune, India

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